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Auteur Mingyong Pang |
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Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network / Ruibin Zhao in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)
[article]
Titre : Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network Type de document : Article/Communication Auteurs : Ruibin Zhao, Auteur ; Mingyong Pang, Auteur ; Jidong Wang, Auteur Année de publication : 2018 Article en page(s) : pp 960 - 979 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] classification
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] régression
[Termes IGN] réseau neuronal convolutif
[Termes IGN] semis de pointsRésumé : (Auteur) Point cloud classification plays a critical role in many applications of airborne light detection and ranging (LiDAR) data. In this paper, we present a deep feature-based method for accurately classifying multiple ground objects from airborne LiDAR point clouds. With several selected attributes of LiDAR point clouds, our method first creates a group of multi-scale contextual images for each point in the data using interpolation. Taking the contextual images as inputs, a multi-scale convolutional neural network (MCNN) is then designed and trained to learn the deep features of LiDAR points across various scales. A softmax regression classifier (SRC) is finally employed to generate classification results of the data with a combination of the deep features learned from various scales. Compared with most of traditional classification methods, which often require users to manually define a group of complex discriminant rules or extract a set of classification features, the proposed method has the ability to automatically learn the deep features and generate more accurate classification results. The performance of our method is evaluated qualitatively and quantitatively using the International Society for Photogrammetry and Remote Sensing benchmark dataset, and the experimental results indicate that our method can effectively distinguish eight types of ground objects, including low vegetation, impervious surface, car, fence/hedge, roof, facade, shrub and tree, and achieves a higher accuracy than other existing methods. Numéro de notice : A2018-196 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1431840 Date de publication en ligne : 15/02/2018 En ligne : https://doi.org/10.1080/13658816.2018.1431840 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89861
in International journal of geographical information science IJGIS > vol 32 n° 5-6 (May - June 2018) . - pp 960 - 979[article]Exemplaires(1)
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